Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations6999
Missing cells203
Missing cells (%)0.2%
Duplicate rows493
Duplicate rows (%)7.0%
Total size in memory711.0 KiB
Average record size in memory104.0 B

Variable types

Text2
Numeric7
Categorical4

Alerts

Dataset has 493 (7.0%) duplicate rowsDuplicates
engine is highly overall correlated with max_power and 1 other fieldsHigh correlation
km_driven is highly overall correlated with yearHigh correlation
max_power is highly overall correlated with engine and 2 other fieldsHigh correlation
seats is highly overall correlated with engineHigh correlation
selling_price is highly overall correlated with max_power and 2 other fieldsHigh correlation
transmission is highly overall correlated with max_power and 1 other fieldsHigh correlation
year is highly overall correlated with km_driven and 1 other fieldsHigh correlation
seller_type is highly imbalanced (51.8%) Imbalance
torque has 203 (2.9%) missing values Missing

Reproduction

Analysis started2024-12-03 19:46:10.054480
Analysis finished2024-12-03 19:46:26.391366
Duration16.34 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

name
Text

Distinct1924
Distinct (%)27.5%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:26.832092image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length54
Median length42
Mean length25.227747
Min length11

Characters and Unicode

Total characters176569
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique804 ?
Unique (%)11.5%

Sample

1st rowMaruti Swift Dzire VDI
2nd rowSkoda Rapid 1.5 TDI Ambition
3rd rowHyundai i20 Sportz Diesel
4th rowMaruti Swift VXI BSIII
5th rowHyundai Xcent 1.2 VTVT E Plus
ValueCountFrequency (%)
maruti 2126
 
6.4%
hyundai 1197
 
3.6%
swift 689
 
2.1%
mahindra 666
 
2.0%
bsiv 622
 
1.9%
tata 611
 
1.8%
diesel 583
 
1.8%
1.2 505
 
1.5%
vxi 485
 
1.5%
plus 459
 
1.4%
Other values (828) 25224
76.1%
2024-12-03T19:46:27.751893image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26169
 
14.8%
a 12741
 
7.2%
i 11534
 
6.5%
t 8812
 
5.0%
r 7759
 
4.4%
o 7165
 
4.1%
n 6573
 
3.7%
e 6564
 
3.7%
u 5091
 
2.9%
S 4755
 
2.7%
Other values (58) 79406
45.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 176569
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
26169
 
14.8%
a 12741
 
7.2%
i 11534
 
6.5%
t 8812
 
5.0%
r 7759
 
4.4%
o 7165
 
4.1%
n 6573
 
3.7%
e 6564
 
3.7%
u 5091
 
2.9%
S 4755
 
2.7%
Other values (58) 79406
45.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 176569
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
26169
 
14.8%
a 12741
 
7.2%
i 11534
 
6.5%
t 8812
 
5.0%
r 7759
 
4.4%
o 7165
 
4.1%
n 6573
 
3.7%
e 6564
 
3.7%
u 5091
 
2.9%
S 4755
 
2.7%
Other values (58) 79406
45.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 176569
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
26169
 
14.8%
a 12741
 
7.2%
i 11534
 
6.5%
t 8812
 
5.0%
r 7759
 
4.4%
o 7165
 
4.1%
n 6573
 
3.7%
e 6564
 
3.7%
u 5091
 
2.9%
S 4755
 
2.7%
Other values (58) 79406
45.0%

year
Real number (ℝ)

High correlation 

Distinct29
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.8184
Minimum1983
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:28.074260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1983
5-th percentile2006
Q12011
median2015
Q32017
95-th percentile2019
Maximum2020
Range37
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.0530948
Coefficient of variation (CV)0.0020126417
Kurtosis1.7672148
Mean2013.8184
Median Absolute Deviation (MAD)3
Skewness-1.0773023
Sum14094715
Variance16.427578
MonotonicityNot monotonic
2024-12-03T19:46:28.363221image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2017 870
12.4%
2016 736
10.5%
2018 704
10.1%
2015 662
9.5%
2013 581
8.3%
2012 563
8.0%
2014 532
7.6%
2019 511
7.3%
2011 499
7.1%
2010 336
 
4.8%
Other values (19) 1005
14.4%
ValueCountFrequency (%)
1983 1
 
< 0.1%
1991 1
 
< 0.1%
1994 3
 
< 0.1%
1995 1
 
< 0.1%
1996 3
 
< 0.1%
1997 10
0.1%
1998 9
0.1%
1999 13
0.2%
2000 21
0.3%
2001 8
 
0.1%
ValueCountFrequency (%)
2020 69
 
1.0%
2019 511
7.3%
2018 704
10.1%
2017 870
12.4%
2016 736
10.5%
2015 662
9.5%
2014 532
7.6%
2013 581
8.3%
2012 563
8.0%
2011 499
7.1%

selling_price
Real number (ℝ)

High correlation 

Distinct637
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean639515.2
Minimum29999
Maximum10000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:28.718653image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum29999
5-th percentile110000
Q1254999
median450000
Q3675000
95-th percentile1925000
Maximum10000000
Range9970001
Interquartile range (IQR)420001

Descriptive statistics

Standard deviation808941.91
Coefficient of variation (CV)1.2649299
Kurtosis21.308644
Mean639515.2
Median Absolute Deviation (MAD)200000
Skewness4.2107557
Sum4.4759669 × 109
Variance6.5438702 × 1011
MonotonicityNot monotonic
2024-12-03T19:46:29.075379image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000 196
 
2.8%
600000 181
 
2.6%
450000 178
 
2.5%
350000 178
 
2.5%
550000 175
 
2.5%
650000 161
 
2.3%
400000 153
 
2.2%
250000 153
 
2.2%
500000 152
 
2.2%
200000 138
 
2.0%
Other values (627) 5334
76.2%
ValueCountFrequency (%)
29999 1
 
< 0.1%
30000 2
 
< 0.1%
31504 1
 
< 0.1%
35000 3
 
< 0.1%
39000 1
 
< 0.1%
40000 12
0.2%
42000 2
 
< 0.1%
45000 16
0.2%
45957 2
 
< 0.1%
50000 15
0.2%
ValueCountFrequency (%)
10000000 1
 
< 0.1%
7200000 1
 
< 0.1%
6523000 1
 
< 0.1%
6223000 1
 
< 0.1%
6000000 4
 
0.1%
5923000 1
 
< 0.1%
5850000 1
 
< 0.1%
5830000 2
 
< 0.1%
5800000 2
 
< 0.1%
5500000 27
0.4%

km_driven
Real number (ℝ)

High correlation 

Distinct827
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69584.616
Minimum1
Maximum2360457
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:29.828797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9000
Q135000
median60000
Q397000
95-th percentile150000
Maximum2360457
Range2360456
Interquartile range (IQR)62000

Descriptive statistics

Standard deviation57724.002
Coefficient of variation (CV)0.82955121
Kurtosis410.79799
Mean69584.616
Median Absolute Deviation (MAD)30000
Skewness12.069152
Sum4.8702272 × 108
Variance3.3320604 × 109
MonotonicityNot monotonic
2024-12-03T19:46:30.154144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000 464
 
6.6%
70000 389
 
5.6%
80000 387
 
5.5%
60000 367
 
5.2%
50000 343
 
4.9%
100000 306
 
4.4%
90000 291
 
4.2%
110000 264
 
3.8%
40000 260
 
3.7%
30000 215
 
3.1%
Other values (817) 3713
53.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
1000 6
 
0.1%
1300 1
 
< 0.1%
1303 4
 
0.1%
1500 3
 
< 0.1%
1600 1
 
< 0.1%
1620 1
 
< 0.1%
2000 28
0.4%
2118 1
 
< 0.1%
2136 1
 
< 0.1%
ValueCountFrequency (%)
2360457 1
< 0.1%
1500000 1
< 0.1%
577414 1
< 0.1%
500000 2
< 0.1%
475000 1
< 0.1%
440000 1
< 0.1%
426000 1
< 0.1%
380000 1
< 0.1%
376412 1
< 0.1%
370000 1
< 0.1%

fuel
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Diesel
3793 
Petrol
3120 
CNG
 
52
LPG
 
34

Length

Max length6
Median length6
Mean length5.9631376
Min length3

Characters and Unicode

Total characters41736
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowDiesel
3rd rowDiesel
4th rowPetrol
5th rowPetrol

Common Values

ValueCountFrequency (%)
Diesel 3793
54.2%
Petrol 3120
44.6%
CNG 52
 
0.7%
LPG 34
 
0.5%

Length

2024-12-03T19:46:30.454341image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T19:46:30.835491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3793
54.2%
petrol 3120
44.6%
cng 52
 
0.7%
lpg 34
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 10706
25.7%
l 6913
16.6%
D 3793
 
9.1%
i 3793
 
9.1%
s 3793
 
9.1%
P 3154
 
7.6%
t 3120
 
7.5%
r 3120
 
7.5%
o 3120
 
7.5%
G 86
 
0.2%
Other values (3) 138
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 41736
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10706
25.7%
l 6913
16.6%
D 3793
 
9.1%
i 3793
 
9.1%
s 3793
 
9.1%
P 3154
 
7.6%
t 3120
 
7.5%
r 3120
 
7.5%
o 3120
 
7.5%
G 86
 
0.2%
Other values (3) 138
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 41736
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10706
25.7%
l 6913
16.6%
D 3793
 
9.1%
i 3793
 
9.1%
s 3793
 
9.1%
P 3154
 
7.6%
t 3120
 
7.5%
r 3120
 
7.5%
o 3120
 
7.5%
G 86
 
0.2%
Other values (3) 138
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 41736
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10706
25.7%
l 6913
16.6%
D 3793
 
9.1%
i 3793
 
9.1%
s 3793
 
9.1%
P 3154
 
7.6%
t 3120
 
7.5%
r 3120
 
7.5%
o 3120
 
7.5%
G 86
 
0.2%
Other values (3) 138
 
0.3%

seller_type
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Individual
5826 
Dealer
967 
Trustmark Dealer
 
206

Length

Max length16
Median length10
Mean length9.6239463
Min length6

Characters and Unicode

Total characters67358
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual
2nd rowIndividual
3rd rowIndividual
4th rowIndividual
5th rowIndividual

Common Values

ValueCountFrequency (%)
Individual 5826
83.2%
Dealer 967
 
13.8%
Trustmark Dealer 206
 
2.9%

Length

2024-12-03T19:46:31.375835image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T19:46:31.723113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
individual 5826
80.9%
dealer 1173
 
16.3%
trustmark 206
 
2.9%

Most occurring characters

ValueCountFrequency (%)
d 11652
17.3%
i 11652
17.3%
a 7205
10.7%
l 6999
10.4%
u 6032
9.0%
I 5826
8.6%
v 5826
8.6%
n 5826
8.6%
e 2346
 
3.5%
r 1585
 
2.4%
Other values (7) 2409
 
3.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 67358
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
d 11652
17.3%
i 11652
17.3%
a 7205
10.7%
l 6999
10.4%
u 6032
9.0%
I 5826
8.6%
v 5826
8.6%
n 5826
8.6%
e 2346
 
3.5%
r 1585
 
2.4%
Other values (7) 2409
 
3.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 67358
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
d 11652
17.3%
i 11652
17.3%
a 7205
10.7%
l 6999
10.4%
u 6032
9.0%
I 5826
8.6%
v 5826
8.6%
n 5826
8.6%
e 2346
 
3.5%
r 1585
 
2.4%
Other values (7) 2409
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 67358
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
d 11652
17.3%
i 11652
17.3%
a 7205
10.7%
l 6999
10.4%
u 6032
9.0%
I 5826
8.6%
v 5826
8.6%
n 5826
8.6%
e 2346
 
3.5%
r 1585
 
2.4%
Other values (7) 2409
 
3.6%

transmission
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
Manual
6095 
Automatic
904 

Length

Max length9
Median length6
Mean length6.3874839
Min length6

Characters and Unicode

Total characters44706
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowManual

Common Values

ValueCountFrequency (%)
Manual 6095
87.1%
Automatic 904
 
12.9%

Length

2024-12-03T19:46:32.141995image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T19:46:32.495267image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 6095
87.1%
automatic 904
 
12.9%

Most occurring characters

ValueCountFrequency (%)
a 13094
29.3%
u 6999
15.7%
M 6095
13.6%
n 6095
13.6%
l 6095
13.6%
t 1808
 
4.0%
A 904
 
2.0%
o 904
 
2.0%
m 904
 
2.0%
i 904
 
2.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 44706
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 13094
29.3%
u 6999
15.7%
M 6095
13.6%
n 6095
13.6%
l 6095
13.6%
t 1808
 
4.0%
A 904
 
2.0%
o 904
 
2.0%
m 904
 
2.0%
i 904
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 44706
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 13094
29.3%
u 6999
15.7%
M 6095
13.6%
n 6095
13.6%
l 6095
13.6%
t 1808
 
4.0%
A 904
 
2.0%
o 904
 
2.0%
m 904
 
2.0%
i 904
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 44706
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 13094
29.3%
u 6999
15.7%
M 6095
13.6%
n 6095
13.6%
l 6095
13.6%
t 1808
 
4.0%
A 904
 
2.0%
o 904
 
2.0%
m 904
 
2.0%
i 904
 
2.0%

owner
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size54.8 KiB
First Owner
4587 
Second Owner
1791 
Third Owner
473 
Fourth & Above Owner
 
144
Test Drive Car
 
4

Length

Max length20
Median length11
Mean length11.442778
Min length11

Characters and Unicode

Total characters80088
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst Owner
2nd rowSecond Owner
3rd rowFirst Owner
4th rowFirst Owner
5th rowFirst Owner

Common Values

ValueCountFrequency (%)
First Owner 4587
65.5%
Second Owner 1791
 
25.6%
Third Owner 473
 
6.8%
Fourth & Above Owner 144
 
2.1%
Test Drive Car 4
 
0.1%

Length

2024-12-03T19:46:33.026731image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-03T19:46:33.517685image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
owner 6995
49.0%
first 4587
32.1%
second 1791
 
12.5%
third 473
 
3.3%
fourth 144
 
1.0%
144
 
1.0%
above 144
 
1.0%
test 4
 
< 0.1%
drive 4
 
< 0.1%
car 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
r 12207
15.2%
e 8938
11.2%
n 8786
11.0%
7291
9.1%
O 6995
8.7%
w 6995
8.7%
i 5064
6.3%
t 4735
 
5.9%
F 4731
 
5.9%
s 4591
 
5.7%
Other values (14) 9755
12.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 12207
15.2%
e 8938
11.2%
n 8786
11.0%
7291
9.1%
O 6995
8.7%
w 6995
8.7%
i 5064
6.3%
t 4735
 
5.9%
F 4731
 
5.9%
s 4591
 
5.7%
Other values (14) 9755
12.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 12207
15.2%
e 8938
11.2%
n 8786
11.0%
7291
9.1%
O 6995
8.7%
w 6995
8.7%
i 5064
6.3%
t 4735
 
5.9%
F 4731
 
5.9%
s 4591
 
5.7%
Other values (14) 9755
12.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 12207
15.2%
e 8938
11.2%
n 8786
11.0%
7291
9.1%
O 6995
8.7%
w 6995
8.7%
i 5064
6.3%
t 4735
 
5.9%
F 4731
 
5.9%
s 4591
 
5.7%
Other values (14) 9755
12.2%

mileage
Real number (ℝ)

Distinct375
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.42295
Minimum0
Maximum42
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:34.146326image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12.99
Q116.8
median19.3
Q322.15
95-th percentile25.83
Maximum42
Range42
Interquartile range (IQR)5.35

Descriptive statistics

Standard deviation3.9869306
Coefficient of variation (CV)0.20526905
Kurtosis0.78610405
Mean19.42295
Median Absolute Deviation (MAD)2.7
Skewness-0.15084575
Sum135941.23
Variance15.895616
MonotonicityNot monotonic
2024-12-03T19:46:34.566270image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.3 259
 
3.7%
18.9 197
 
2.8%
19.7 150
 
2.1%
18.6 139
 
2.0%
21.1 131
 
1.9%
17 116
 
1.7%
15.96 105
 
1.5%
17.8 101
 
1.4%
22 99
 
1.4%
12.99 95
 
1.4%
Other values (365) 5607
80.1%
ValueCountFrequency (%)
0 16
0.2%
9 4
 
0.1%
9.5 5
 
0.1%
10 2
 
< 0.1%
10.1 2
 
< 0.1%
10.5 14
0.2%
10.71 1
 
< 0.1%
10.75 2
 
< 0.1%
10.8 1
 
< 0.1%
10.9 4
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
33.44 3
 
< 0.1%
33 1
 
< 0.1%
32.52 1
 
< 0.1%
30.46 2
 
< 0.1%
28.4 80
1.1%
28.09 36
0.5%
27.62 5
 
0.1%
27.4 6
 
0.1%
27.39 49
0.7%

engine
Real number (ℝ)

High correlation 

Distinct120
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1452.2569
Minimum624
Maximum3604
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:34.886471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum624
5-th percentile796
Q11197
median1248
Q31582
95-th percentile2498
Maximum3604
Range2980
Interquartile range (IQR)385

Descriptive statistics

Standard deviation495.1513
Coefficient of variation (CV)0.34095297
Kurtosis0.86257694
Mean1452.2569
Median Absolute Deviation (MAD)245
Skewness1.1818336
Sum10164346
Variance245174.81
MonotonicityNot monotonic
2024-12-03T19:46:35.224593image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1248 1087
 
15.5%
1197 715
 
10.2%
998 393
 
5.6%
796 375
 
5.4%
2179 330
 
4.7%
1498 318
 
4.5%
1396 264
 
3.8%
1199 233
 
3.3%
2494 188
 
2.7%
2523 171
 
2.4%
Other values (110) 2925
41.8%
ValueCountFrequency (%)
624 16
 
0.2%
793 5
 
0.1%
796 375
5.4%
799 66
 
0.9%
814 99
 
1.4%
909 2
 
< 0.1%
936 31
 
0.4%
993 24
 
0.3%
995 41
 
0.6%
998 393
5.6%
ValueCountFrequency (%)
3604 5
 
0.1%
3498 1
 
< 0.1%
3198 3
 
< 0.1%
2999 3
 
< 0.1%
2997 2
 
< 0.1%
2993 14
0.2%
2987 9
 
0.1%
2982 28
0.4%
2967 9
 
0.1%
2956 15
0.2%

max_power
Real number (ℝ)

High correlation 

Distinct312
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91.302923
Minimum32.8
Maximum400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:35.548095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum32.8
5-th percentile47.3
Q168.085
median82
Q3100.6
95-th percentile171.5
Maximum400
Range367.2
Interquartile range (IQR)32.515

Descriptive statistics

Standard deviation35.24864
Coefficient of variation (CV)0.38606256
Kurtosis4.1427826
Mean91.302923
Median Absolute Deviation (MAD)14.95
Skewness1.7026217
Sum639029.16
Variance1242.4666
MonotonicityNot monotonic
2024-12-03T19:46:35.906545image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74 330
 
4.7%
82 300
 
4.3%
81.8 193
 
2.8%
88.5 189
 
2.7%
67 149
 
2.1%
46.3 139
 
2.0%
62.1 130
 
1.9%
67.1 127
 
1.8%
67.04 126
 
1.8%
88.7 125
 
1.8%
Other values (302) 5191
74.2%
ValueCountFrequency (%)
32.8 2
 
< 0.1%
34.2 19
 
0.3%
35 16
 
0.2%
35.5 2
 
< 0.1%
37 74
1.1%
37.48 8
 
0.1%
37.5 6
 
0.1%
38 1
 
< 0.1%
38.4 2
 
< 0.1%
40.3 3
 
< 0.1%
ValueCountFrequency (%)
400 1
 
< 0.1%
282 1
 
< 0.1%
280 5
0.1%
272 1
 
< 0.1%
270.9 3
< 0.1%
265 1
 
< 0.1%
261.4 6
0.1%
258 2
 
< 0.1%
254.8 3
< 0.1%
254.79 1
 
< 0.1%

torque
Text

Missing 

Distinct419
Distinct (%)6.2%
Missing203
Missing (%)2.9%
Memory size54.8 KiB
2024-12-03T19:46:36.463407image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length27
Median length25
Mean length16.250441
Min length5

Characters and Unicode

Total characters110438
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)1.5%

Sample

1st row190Nm@ 2000rpm
2nd row250Nm@ 1500-2500rpm
3rd row22.4 kgm at 1750-2750rpm
4th row11.5@ 4,500(kgm@ rpm)
5th row113.75nm@ 4000rpm
ValueCountFrequency (%)
4000rpm 740
 
5.2%
2000rpm 688
 
4.8%
3500rpm 630
 
4.4%
200nm 596
 
4.2%
190nm 528
 
3.7%
1750rpm 483
 
3.4%
rpm 452
 
3.1%
90nm 353
 
2.5%
3000rpm 285
 
2.0%
4200rpm 269
 
1.9%
Other values (409) 9336
65.0%
2024-12-03T19:46:37.329548image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 22916
20.8%
m 13512
12.2%
7591
 
6.9%
1 7473
 
6.8%
@ 6915
 
6.3%
r 6744
 
6.1%
p 6744
 
6.1%
N 6130
 
5.6%
2 5896
 
5.3%
5 5328
 
4.8%
Other values (24) 21189
19.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 110438
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 22916
20.8%
m 13512
12.2%
7591
 
6.9%
1 7473
 
6.8%
@ 6915
 
6.3%
r 6744
 
6.1%
p 6744
 
6.1%
N 6130
 
5.6%
2 5896
 
5.3%
5 5328
 
4.8%
Other values (24) 21189
19.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 110438
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 22916
20.8%
m 13512
12.2%
7591
 
6.9%
1 7473
 
6.8%
@ 6915
 
6.3%
r 6744
 
6.1%
p 6744
 
6.1%
N 6130
 
5.6%
2 5896
 
5.3%
5 5328
 
4.8%
Other values (24) 21189
19.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 110438
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 22916
20.8%
m 13512
12.2%
7591
 
6.9%
1 7473
 
6.8%
@ 6915
 
6.3%
r 6744
 
6.1%
p 6744
 
6.1%
N 6130
 
5.6%
2 5896
 
5.3%
5 5328
 
4.8%
Other values (24) 21189
19.2%

seats
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.4069153
Minimum2
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size54.8 KiB
2024-12-03T19:46:37.630971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum14
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.95430803
Coefficient of variation (CV)0.17649769
Kurtosis4.2811184
Mean5.4069153
Median Absolute Deviation (MAD)0
Skewness2.0598916
Sum37843
Variance0.91070382
MonotonicityNot monotonic
2024-12-03T19:46:37.915792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 5595
79.9%
7 944
 
13.5%
8 208
 
3.0%
4 104
 
1.5%
9 72
 
1.0%
6 54
 
0.8%
10 19
 
0.3%
2 2
 
< 0.1%
14 1
 
< 0.1%
ValueCountFrequency (%)
2 2
 
< 0.1%
4 104
 
1.5%
5 5595
79.9%
6 54
 
0.8%
7 944
 
13.5%
8 208
 
3.0%
9 72
 
1.0%
10 19
 
0.3%
14 1
 
< 0.1%
ValueCountFrequency (%)
14 1
 
< 0.1%
10 19
 
0.3%
9 72
 
1.0%
8 208
 
3.0%
7 944
 
13.5%
6 54
 
0.8%
5 5595
79.9%
4 104
 
1.5%
2 2
 
< 0.1%

Interactions

2024-12-03T19:46:23.776879image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:11.038166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:13.471567image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:15.241264image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:17.307661image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:19.733799image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:22.011785image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:24.048683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:11.265277image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:13.731857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:15.486948image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:17.618197image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:20.169739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:22.273414image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:24.288796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:11.494080image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:13.998194image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:15.759427image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:18.016425image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:20.560691image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:22.507611image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:24.523905image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:11.745619image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:14.229023image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:16.006190image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:18.259141image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:21.039613image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:22.772581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:24.801801image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:12.695046image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:14.457265image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:16.237421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:18.561212image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:21.282224image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:23.001334image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:25.054072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:12.963982image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:14.729094image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:16.512503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:18.932166image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:21.523535image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:23.236225image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:25.302061image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:13.222075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:14.997004image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:16.950722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:19.358320image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:21.778028image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-03T19:46:23.485115image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-03T19:46:38.139386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
enginefuelkm_drivenmax_powermileageownerseatsseller_typeselling_pricetransmissionyear
engine1.0000.4370.2370.732-0.4560.0840.5110.2290.5000.488-0.003
fuel0.4371.0000.0350.2060.2970.0310.2100.1080.1100.0500.122
km_driven0.2370.0351.000-0.033-0.1540.0340.2150.018-0.3580.027-0.620
max_power0.7320.206-0.0331.000-0.3440.0860.2750.2480.6500.5970.213
mileage-0.4560.297-0.154-0.3441.0000.089-0.4340.072-0.0280.2140.294
owner0.0840.0310.0340.0860.0891.0000.0290.1690.3640.1690.269
seats0.5110.2100.2150.275-0.4340.0291.0000.0610.2690.0710.005
seller_type0.2290.1080.0180.2480.0720.1690.0611.0000.2820.3730.185
selling_price0.5000.110-0.3580.650-0.0280.3640.2690.2821.0000.5860.714
transmission0.4880.0500.0270.5970.2140.1690.0710.3730.5861.0000.269
year-0.0030.122-0.6200.2130.2940.2690.0050.1850.7140.2691.000

Missing values

2024-12-03T19:46:25.696401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-03T19:46:26.163475image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
0Maruti Swift Dzire VDI2014450000145500DieselIndividualManualFirst Owner23.401248.074.00190Nm@ 2000rpm5.0
1Skoda Rapid 1.5 TDI Ambition2014370000120000DieselIndividualManualSecond Owner21.141498.0103.52250Nm@ 1500-2500rpm5.0
2Hyundai i20 Sportz Diesel2010225000127000DieselIndividualManualFirst Owner23.001396.090.0022.4 kgm at 1750-2750rpm5.0
3Maruti Swift VXI BSIII2007130000120000PetrolIndividualManualFirst Owner16.101298.088.2011.5@ 4,500(kgm@ rpm)5.0
4Hyundai Xcent 1.2 VTVT E Plus201744000045000PetrolIndividualManualFirst Owner20.141197.081.86113.75nm@ 4000rpm5.0
5Maruti Wagon R LXI DUO BSIII200796000175000LPGIndividualManualFirst Owner17.301061.057.507.8@ 4,500(kgm@ rpm)5.0
6Maruti 800 DX BSII2001450005000PetrolIndividualManualSecond Owner16.10796.037.0059Nm@ 2500rpm4.0
7Toyota Etios VXD201135000090000DieselIndividualManualFirst Owner23.591364.067.10170Nm@ 1800-2400rpm5.0
8Ford Figo Diesel Celebration Edition2013200000169000DieselIndividualManualFirst Owner20.001399.068.10160Nm@ 2000rpm5.0
9Renault Duster 110PS Diesel RxL201450000068000DieselIndividualManualSecond Owner19.011461.0108.45248Nm@ 2250rpm5.0
nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats
6989Maruti Swift Dzire VDI201562500050000DieselIndividualManualFirst Owner26.591248.074.00190Nm@ 2000rpm5.0
6990Hyundai i20 Magna201338000025000PetrolIndividualManualFirst Owner18.501197.082.85113.7Nm@ 4000rpm5.0
6991Maruti Wagon R LXI Optional201736000080000PetrolIndividualManualFirst Owner20.51998.067.0490Nm@ 3500rpm5.0
6992Hyundai Santro Xing GLS2008120000191000PetrolIndividualManualFirst Owner17.921086.062.1096.1Nm@ 3000rpm5.0
6993Maruti Wagon R VXI BS IV with ABS201326000050000PetrolIndividualManualSecond Owner18.90998.067.1090Nm@ 3500rpm5.0
6994Hyundai i20 Magna2013320000110000PetrolIndividualManualFirst Owner18.501197.082.85113.7Nm@ 4000rpm5.0
6995Hyundai Verna CRDi SX2007135000119000DieselIndividualManualFourth & Above Owner16.801493.0110.0024@ 1,900-2,750(kgm@ rpm)5.0
6996Maruti Swift Dzire ZDi2009382000120000DieselIndividualManualFirst Owner19.301248.073.90190Nm@ 2000rpm5.0
6997Tata Indigo CR4201329000025000DieselIndividualManualFirst Owner23.571396.070.00140Nm@ 1800-3000rpm5.0
6998Tata Indigo CR4201329000025000DieselIndividualManualFirst Owner23.571396.070.00140Nm@ 1800-3000rpm5.0

Duplicate rows

Most frequently occurring

nameyearselling_pricekm_drivenfuelseller_typetransmissionownermileageenginemax_powertorqueseats# duplicates
64Honda Amaze V CVT Petrol BSIV20197790007032PetrolTrustmark DealerAutomaticFirst Owner19.001199.088.76110Nm@ 4800rpm5.030
192Lexus ES 300h2019515000020000PetrolDealerAutomaticFirst Owner22.372487.0214.56202Nm@ 3600-5200rpm5.030
183Jaguar XF 2.0 Diesel Portfolio2017320000045000DieselDealerAutomaticFirst Owner19.331999.0177.00430Nm@ 1750-2500rpm5.028
467Toyota Innova 2.5 VX (Diesel) 7 Seater201375000079328DieselTrustmark DealerManualSecond Owner12.992494.0100.60200Nm@ 1200-3600rpm7.028
13BMW X4 M Sport X xDrive20d201954000007500DieselDealerAutomaticFirst Owner16.781995.0190.00400Nm@ 1750-2500rpm5.027
255Maruti Baleno Alpha 1.3201874000038817DieselDealerManualFirst Owner27.391248.074.00190Nm@ 2000rpm5.027
306Maruti Swift AMT ZXI201860000069779PetrolDealerAutomaticFirst Owner22.001197.081.80113Nm@ 4200rpm5.027
363Maruti Wagon R LXI201322500058343PetrolTrustmark DealerManualFirst Owner21.79998.067.0590Nm@ 3500rpm5.027
455Toyota Etios VX201762500025538PetrolTrustmark DealerManualFirst Owner16.781496.088.73132Nm@ 3000rpm5.027
114Hyundai Grand i10 1.2 CRDi Sportz201745000056290DieselDealerManualFirst Owner24.001186.073.97190.24nm@ 1750-2250rpm5.026